code: def _extract_relevant_logits(
      self,
      logits: torch.Tensor,
      example: ExampleWithClasses,
      index: int,
  ) -> torch.Tensor:
      example_logits = logits[index]
      # NOTE: we take the last element of the returned token list
      # this is because the tokenizer returns a 1-element list for GPT tokenizers
      # and a 2-element list with start token in the first position for OPT
      # tokenizers
      class_tokens = [
          token[-1]
          for token in self.tokenizer(list(example.classes))[
              "input_ids"
          ]
      ]
      # log_softmax just subtracts a constant, so repeated applications change nothing
      # and there is no point in taking logprobs before focusing on the
      # relevant indices
      relevant_logits = example_logits[class_tokens]
      return relevant_logits
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Union, cast, Sequence
  from typing_extensions import Literal, get_args
  import numpy
  import torch
  import torch.nn.functional
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from accelerate import Accelerator
  from .isp_dataset import ClassificationExample, Example, ExampleWithClasses, LogoddsExample, SequenceProbExample, NumericExample, TaskType
  from .isp_utils import BasicParser
  from .isp_utils import OpenAIModel, OpenaAIModelList
label_desc: Use image node_id 11 for calling the HFModel obj's underlying member method code with example usage: obj._extract_relevant_logits(logits, example, index) and returns: relevant_logits